Inconsistent data formats and fragmented datasets can severely limit the effectiveness of machine learning models. These challenges necessitate data harmonization to produce effectual results. For example, the COVID-19 pandemic revealed the need for harmonized clinical and genomic data to accelerate drug discovery endeavors. AstraZeneca’s AI-driven models benefited from harmonized datasets to identify new potential drugs for COVID-19. Similarly, harmonization was pivotal in Pfizer’s machine learning models to improve clinical trial outcomes. Without harmonized data, these ML-driven breakthroughs in drug development would face roadblocks, and impair innovation and patient outcomes. This blog explores the role and impact of data harmonization on the efficiency and accuracy of ML models in drug development.
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Impact of Data Harmonization on Machine Learning Models in Drug Development
